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Lunar-G2R: Geometry-to-Reflectance Learning for High-Fidelity Lunar BRDF Estimation
Clémentine Grethen,
Nicolas Menga,
Roland Brochard,
Simone Gasparini,
Géraldine Morin,
Jérémy Lebreton,
Manuel Sanchez-Gestido
arXiv preprint, 2026
[arXiv]
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[Dataset]
We propose Lunar-G2R, a geometry-to-reflectance learning framework
for high-fidelity BRDF estimation of the lunar surface, leveraging large-scale
supervised data under diverse illumination and viewing conditions.
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Adapting Stereo Vision From Objects To 3D Lunar Surface Reconstruction with the StereoLunar Dataset
Clémentine Grethen,
Simone Gasparini,
Géraldine Morin,
Jérémy Lebreton,
Lucas Marti,
Manuel Sanchez-Gestido
ICCV "From Street to Space", Honolulu 2025
[Paper]
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[Project Page]
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[StereoLunar Dataset]
We introduce LunarStereo, a photorealistic stereo image dataset of the Moon, and show that fine-tuning the MASt3R model enables accurate 3D reconstruction and pose estimation under lunar conditions, significantly outperforming baselines.
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Mise en place d'une méthode de reconstruction 3D du sol lunaire à partir de plusieurs images dans un contexte d'atterrissage.
Clémentine Grethen,
Simone Gasparini,
Géraldine Morin
Orasis, 2025
HAL
The paper proposes a method for generating realistic images of the lunar surface and a method for 3D reconstruction of the lunar surface based on traditional techniques.
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